函数优化的量子正弦余弦算法

Quantum sine cosine algorithm for function optimization

正弦余弦算法利用正弦和余弦函数对个体位置进行更新与搜索。为避免正弦余弦算法早熟收敛,根据量子进化算法的相关理论和正弦余弦算法原理,设计了一种求解函数优化问题的新型智能算法——量子正弦余弦算法。量子正弦余弦算法利用量子位对个体位置进行编码,以量子旋转门实现对个体最优位置的搜索,并以量子门实现个体的变异,从而避免早熟收敛。通过一系列典型函数优化问题的求解实验并与其他算法作比较进行检验,实验结果表明该算法具有良好的性能。

Sine cosine algorithm（SCA） updates and searches the position of individuals by using the sine and cosine function. This paper proposed a novel population-based optimization algorithm：quantum sine cosine algorithm（QSCA） for soloving function optimization problem. In order to avoid premature convergence of SCA, QSCA used the quantum bit to encode the position of individuals and searched the optimal solution with quantum rotation gate, and adopted quantum gate to mutation. Series of computational experiments on typical benchmark functions compared with that of other algorithms show that the proposed algorithm has a better performance.